catbench 1.1.1

CatBench Framework for Benchmarking Machine Learning Interatomic Potentials in Adsorption Energy Predictions for Heterogeneous Catalysis

CatBench

A benchmarking framework for Machine Learning Interatomic Potentials in heterogeneous catalysis.

CatBench evaluates MLIPs against DFT references across four task types — adsorption energy, surface energy, bulk formation energy, and equation of state — with automated data processing, reproducible calculation workflows, and statistical anomaly detection.

Table of Contents

• Installation
• Quick Start
• Tutorials
• Adsorption Energy Benchmarking
  • Data Preparation
  • Calculation
  • Analysis
  • Output Files
• Relative Energy Benchmarking
  • Surface Energy
  • Bulk Formation Energy
• Equation of State (EOS) Benchmarking
• Configuration Reference
• Citation

Installation

# Basic
pip install catbench

# With D3 dispersion correction (GPU required; CPU-only not currently supported)
pip install catbench[d3]

# Development install from source
git clone https://github.com/JinukMoon/CatBench.git
cd CatBench
pip install -e .

Quick Start

Minimum viable benchmark in 5 lines:

from catbench.adsorption import zenodo_download, AdsorptionCalculation, AdsorptionAnalysis
from your_mlip import YourCalculator

zenodo_download("BM_dataset")                                            # 445 KB
calc = YourCalculator(...)
AdsorptionCalculation([calc] * 3, mlip_name="MyMLIP", benchmark="BM_dataset").run()
AdsorptionAnalysis().analysis()                                          # parity plots + Excel

For an end-to-end walkthrough on the publication's main benchmark (MamunHighT2019) with MACE-MP-0, see the tutorial notebook.

Tutorials

tutorials/catbench_tutorial.ipynb — complete walkthrough on a 20-reaction subset of MamunHighT2019 using MACE-MP-0 as the representative MLIP. Covers Zenodo download, calculator setup, calculation, analysis, and the multi-MLIP comparison workflow. Runs in about 10 minutes on a Colab T4 GPU.

Adsorption Energy Benchmarking

Data Preparation

CatBench supports three data sources, ordered by convenience.

Option A: Pre-formatted Zenodo Download (Fastest, Recommended)

Five main benchmark datasets from the CatBench publication are hosted as pre-processed JSON files on Zenodo (DOI: 10.5281/zenodo.17157086):

from catbench.adsorption import zenodo_download, list_zenodo_benchmarks

list_zenodo_benchmarks()
# → ['BM_dataset', 'ComerGeneralized2024', 'FG_dataset', 'KHLOHC_origin', 'MamunHighT2019']

zenodo_download("MamunHighT2019")   # writes raw_data/MamunHighT2019_adsorption.json

Benchmark	Size	Description
MamunHighT2019	195 MB	45,130 small-molecule adsorptions on 2,035 bimetallic alloys
FG_dataset	15 MB	2,651 C1–C10 organic molecules on transition metals
KHLOHC_origin	11 MB	Liquid organic hydrogen carrier adsorption (fine-tuning)
ComerGeneralized2024	2 MB	325 adsorptions on metal oxide surfaces
BM_dataset	0.4 MB	32 industrial large molecules (biomass, polyurethane, plastics)

Option B: CatHub Database

For benchmarks not on Zenodo, download and preprocess directly from CatHub:

from catbench.adsorption import cathub_preprocessing

cathub_preprocessing("MamunHighT2019")

# Multiple datasets with adsorbate name unification:
cathub_preprocessing(
    ["MamunHighT2019", "AraComputational2022"],
    adsorbate_integration={"HO": "OH", "O2H": "OOH"},
)

Downloads are deterministic (stable order: "id" pagination + id-based dedup) and fixed-atom constraints are handled automatically: CatHub's deposited constraints are kept as-is, and for datasets where CatHub omits them the fixed set is inferred from geometry (clean slab vs adslab — atoms that do not move were held fixed) and injected as FixAtoms. A genuinely unconstrained slab is left free. So every preprocessed dataset is self-describing and runs correctly out of the box.

Option C: User VASP Data

Note: Fixed-atom constraints are taken from the data automatically — your VASP Selective-Dynamics (T/F) flags are read into FixAtoms on the CONTCAR and applied during relaxation.

Warning: vasp_preprocessing deletes every file except CONTCAR and OSZICAR to save disk space. Always run it on a copy of your original VASP output.

Organize the data as follows. gas, slab, and the <name>gas pattern are reserved; everything else is arbitrary:

your_dataset_name/
├── gas/
│   ├── H2gas/     { CONTCAR, OSZICAR }    # gas-reference folder, must end with "gas"
│   └── H2Ogas/    { CONTCAR, OSZICAR }
├── system_A/
│   ├── slab/      { CONTCAR, OSZICAR }    # reserved name (clean surface)
│   ├── H/
│   │   ├── site_0/  { CONTCAR, OSZICAR }  # any name for site variants
│   │   └── site_1/  { CONTCAR, OSZICAR }
│   └── OH/
│       └── ...
└── system_B/
    └── ...

Declare the reaction stoichiometry and preprocess:

from catbench.adsorption import vasp_preprocessing

coeff_setting = {
    "H":  {"slab": -1, "adslab": 1, "H2gas": -1/2},
    "OH": {"slab": -1, "adslab": 1, "H2gas": +1/2, "H2Ogas": -1},
}
vasp_preprocessing("your_dataset_name", coeff_setting)
# → raw_data/your_dataset_name_adsorption.json

The keys "slab" and "adslab" are required literals on every entry; all other keys are gas-phase references and must end with "gas". vasp_preprocessing validates these rules before deleting anything.

Calculation

AdsorptionCalculation takes a list of calculators. Running the same calculator multiple times provides reproducibility statistics that the analysis step uses for anomaly detection.

from catbench.adsorption import AdsorptionCalculation
from your_mlip import YourCalculator

calc = YourCalculator(...)

AdsorptionCalculation(
    [calc] * 3,                    # 3 reproducibility seeds
    mlip_name="YourMLIP",          # free-form label — folder name under result/, display name in plots
    benchmark="dataset_name",
    # save_files=False,            # skip trajectory + log files to save disk space
).run()

D3 dispersion correction (GPU required):

from catbench.dispersion import DispersionCorrection

d3 = DispersionCorrection()                     # Becke-Johnson damping + PBE by default
calc_d3 = d3.apply(YourCalculator(...))
AdsorptionCalculation([calc_d3] * 3, mlip_name="YourMLIP_D3", benchmark="dataset_name").run()

OC20 mode for MLIPs that predict adsorption energy directly:

AdsorptionCalculation(
    [oc20_calc] * 3, mode="oc20", mlip_name="OC20_MLIP", benchmark="dataset_name",
).run()

See the Configuration Reference for all options, and the tutorial notebook for an end-to-end runnable example.

Analysis

from catbench.adsorption import AdsorptionAnalysis

AdsorptionAnalysis().analysis()    # auto-detects every MLIP under ./result/

This produces:

• Parity plots under ./plot/<mlip_name>/{mono,multi}/ — mono/total.png aggregates all reactions; multi/total.png colors by adsorbate.
• Excel report ./{cwd_name}_Benchmarking_Analysis.xlsx with MAE, RMSE, anomaly breakdown, ADwT, AMDwT, and timings across every MLIP in ./result/.

Every data point is classified into Normal, Migration, Energy Anom., Unphys. Relax, or Reprod. Fail. Thresholds are configurable — see the Configuration Reference.

Threshold sensitivity:

AdsorptionAnalysis().threshold_sensitivity_analysis()   # displacement + bond-length by default

This generates stacked-area charts showing how anomaly-classification rates change with threshold values.

Output Files

Parity plots

Mono — all reactions combined	Multi — colored by adsorbate

Excel report

The Excel workbook has three sheet types. Example numbers from the paper:

Main comparison sheet — one row per MLIP:

MLIP	Normal (%)	Anomaly (%)	MAE_total (eV)	MAE_normal (eV)	ADwT (%)	AMDwT (%)	Time/step (ms)
MLIP_A	77.25	14.39	1.118	0.316	77.98	84.71	125.3
MLIP_B	74.22	16.84	0.667	0.512	69.66	80.80	89.7
MLIP_C	80.18	13.51	0.917	0.241	78.97	86.79	156.8
...	...	...	...	...	...	...	...

Additional sheets (click to expand)

Anomaly breakdown — counts per anomaly category per MLIP:

MLIP	Normal	Migration	Energy Anom.	Unphys. Relax	Reprod. Fail
MLIP_A	34,869	3,774	590	3,845	2,052
MLIP_B	33,503	4,035	834	5,221	1,537
...	...	...	...	...	...

Per-MLIP adsorbate sheets — one sheet per MLIP, one row per adsorbate:

Adsorbate	Normal	Anomaly	MAE_total (eV)	MAE_normal (eV)	ADwT (%)	AMDwT (%)
H	1,247	89	0.891	0.234	89.3	93.4
OH	1,156	124	1.045	0.298	82.7	87.1
...	...	...	...	...	...	...

Threshold sensitivity charts

Displacement threshold	Bond-length change threshold

Relative Energy Benchmarking

Same data → calculation → analysis shape as Adsorption, but with a single calculator (no reproducibility seeds) and a task_type dispatch.

Surface Energy

Warning: Preprocessing deletes all files except CONTCAR and OSZICAR. Always work on a copy.

Layout — per material, one bulk/ and one slab/:

your_surface_data/
├── Material_1/
│   ├── bulk/  { CONTCAR, OSZICAR }
│   └── slab/  { CONTCAR, OSZICAR }
├── Material_2/
│   └── ...

from catbench.relative.surface_energy.data import surface_energy_vasp_preprocessing
from catbench.relative import SurfaceEnergyCalculation, RelativeEnergyAnalysis

surface_energy_vasp_preprocessing("your_surface_data")
SurfaceEnergyCalculation(calculator=calc, benchmark="your_surface_data", mlip_name="MyMLIP").run()
RelativeEnergyAnalysis(task_type="surface").analysis()

The Excel report provides MAE, RMSE, and max error (J/m2) across all surfaces per MLIP.

Bulk Formation Energy

Warning: Preprocessing deletes all files except CONTCAR and OSZICAR.

Layout — bulk_compounds/ and elements/ side-by-side:

your_formation_data/
├── bulk_compounds/
│   ├── Compound_1/  { CONTCAR, OSZICAR }
│   └── Compound_2/  { CONTCAR, OSZICAR }
└── elements/
    ├── Element_A/   { CONTCAR, OSZICAR }
    ├── Element_B/   { CONTCAR, OSZICAR }
    └── Element_C/   { CONTCAR, OSZICAR }

from catbench.relative.bulk_formation.data import bulk_formation_vasp_preprocessing
from catbench.relative import BulkFormationCalculation, RelativeEnergyAnalysis

coeff_setting = {
    "Compound_1": {"bulk": 1, "Element_A": -1, "Element_C": -1/2},
    "Compound_2": {"bulk": 1, "Element_B": -2, "Element_C": -3/2},
}
bulk_formation_vasp_preprocessing("your_formation_data", coeff_setting)
BulkFormationCalculation(calculator=calc, benchmark="your_formation_data", mlip_name="MyMLIP").run()
RelativeEnergyAnalysis(task_type="bulk_formation").analysis()

Equation of State (EOS) Benchmarking

Each material has N volume-point subfolders named 0, 1, …, N:

your_eos_data/
├── Material_1/
│   ├── 0/   { CONTCAR, OSZICAR }    # smallest volume
│   ├── 1/   { CONTCAR, OSZICAR }
│   └── ...   (up to 10, typically)
├── Material_2/
│   └── ...

from catbench.eos import eos_vasp_preprocessing, EOSCalculation, EOSAnalysis

eos_vasp_preprocessing("your_eos_data")
EOSCalculation(calculator=calc, mlip_name="MyMLIP", benchmark="your_eos_data").run()
EOSAnalysis().analysis()

The Excel report includes Birch-Murnaghan fits with bulk modulus (B0), equilibrium volume (V0), and derivative (B0'):

MLIP	RMSE (eV)	MAE (eV)	VASP B0 (GPa)	MLIP B0 (GPa)	B0 Error (GPa)	VASP V0 (A^3)	MLIP V0 (A^3)	V0 Error (A^3)
MLIP_A	0.634	0.462	80.53	102.59	22.06	475.37	469.42	5.95
MLIP_B	0.411	0.318	80.53	72.29	8.24	475.37	478.51	3.13
MLIP_C	0.444	0.350	80.53	88.02	7.49	475.37	470.70	4.67
...	...	...	...	...	...	...	...	...

Configuration Reference

Options are grouped into Required, Commonly tuned, and Advanced (collapsed). Required parameters must be passed at construction; the rest have sensible defaults and can be overridden as needed.

AdsorptionCalculation

Required

Parameter	Description	Type
mlip_name	Free-form label. Used as the folder name under result/ and as the display name in plots and Excel sheets.	str
benchmark	Dataset name; matches raw_data/{benchmark}_adsorption.json.	str

Commonly tuned

Parameter	Description	Default
save_files	If False, skips trajectory + log files to save disk space.	True
f_crit_relax	Force convergence criterion (eV/A).	0.05
n_crit_relax	Max optimization steps per structure.	999
mode	"basic" (relaxation + references) or "oc20" (direct E_ads prediction).	"basic"

Advanced

Parameter	Description	Default
damping	Optimization damping factor.	1.0
optimizer	ASE optimizer: LBFGS / LBFGSLineSearch / BFGS / BFGSLineSearch / GPMin / MDMin / FIRE.	"LBFGS"
save_step	Save interval for result.json during long runs.	50
chemical_bond_cutoff	Cutoff distance for bond-change detection (A).	6.0

AdsorptionAnalysis

Commonly tuned

Parameter	Description	Default
mlip_list	Limit analysis to specific MLIPs.	Auto-detect all under ./result/
target_adsorbates	Analyze only these adsorbates.	All
exclude_adsorbates	Skip these adsorbates.	None
disp_thrs	Displacement anomaly threshold (A).	0.5
energy_thrs	Energy anomaly threshold (eV).	2.0
reproduction_thrs	Cross-seed reproducibility threshold (eV).	0.2
bond_length_change_threshold	Bond-length-change anomaly threshold (fraction).	0.2
energy_cutoff	Exclude reference energies above this value (eV).	None
mlip_name_map	Display-name overrides, e.g. {"MACE-MP-0": "MACE"}.	{}
font_setting	[path_to_ttf, family_name] for custom plot font.	False

Advanced — paths, plot styling, font sizes

Parameter	Description	Default
calculating_path	Path to results directory.	./result
benchmarking_name	Output file prefix.	CWD name
time_unit	"s", "ms", or "us".	"ms"
plot_enabled	Generate plots.	True
figsize	Figure size (width, height) in inches.	(9, 8)
dpi	Plot DPI.	300
mark_size	Marker size.	100
linewidths	Line width.	1.5
min, max	Plot axis limits.	None
tick_bins, tick_decimal_places	Tick control.	6, 1
tick_labelsize	Tick-label font size.	25
xlabel_fontsize, ylabel_fontsize	Axis-label font sizes.	40, 40
mae_text_fontsize	MAE-text font size.	30
legend_fontsize, comparison_legend_fontsize	Legend font sizes.	25, 15
threshold_xlabel_fontsize, threshold_ylabel_fontsize	Threshold-plot label sizes.	40, 40
legend_off, mae_text_off, error_bar_display	Display toggles.	False
xlabel_off, ylabel_off, grid	Display toggles.	False
specific_color	Single-MLIP plot color.	"#2077B5"

DispersionCorrection

Parameter	Description	Default
damping_type	"damp_bj" (Becke-Johnson, recommended) or "damp_zero".	"damp_bj"
functional_name	DFT functional for D3 parameters (pbe, scan, b3lyp, hse06, ...).	"pbe"
vdw_cutoff	van der Waals cutoff (au^2).	9000
cn_cutoff	Coordination number cutoff (au^2).	1600

Relative energy and EOS classes

SurfaceEnergyCalculation, BulkFormationCalculation, and EOSCalculation all require calculator, benchmark, and mlip_name, and accept f_crit_relax and n_crit_relax for optimization control.

RelativeEnergyAnalysis requires task_type ("surface", "bulk_formation", or "custom") and accepts the same plotting options as AdsorptionAnalysis.

EOSAnalysis advanced options

Parameter	Description	Default
calculating_path	Results directory.	./result
plot_path	Plot output directory.	./plot
benchmark	Dataset name.	CWD name
mlip_list	MLIPs to analyze.	Auto-detect
figsize	Plot dimensions.	(9, 8)
dpi	Plot DPI.	300
mark_size	Marker size.	100
x_tick_bins, y_tick_bins	Tick bins.	5, 5
tick_decimal_places, tick_labelsize	Tick control.	1, 25
xlabel_fontsize, ylabel_fontsize	Axis-label font sizes.	40, 40
legend_fontsize, comparison_legend_fontsize	Legend font sizes.	25, 15
grid	Show grid.	False
font_setting	Custom font [path, family].	False

Citation

@article{catbench2025,
  title={CatBench Framework for Benchmarking Machine Learning Interatomic Potentials in Adsorption Energy Predictions for Heterogeneous Catalysis},
  author={Moon, Jinuk and Jeon, Uchan and Choung, Seokhyun and Han, Jeong Woo},
  journal={Cell Reports Physical Science},
  volume={6},
  pages={102968},
  year={2025},
  doi={10.1016/j.xcrp.2025.102968}
}

License

MIT — see LICENSE.

Contact

Jinuk Moon · [e-mail]
Jeong Woo Han · [e-mail]
Seoul National University

For bug reports, feature requests, and contributions: GitHub repository.